The Impact of Online Word of Mouth (e-WOM) on End-User Purchasing Intentions: A Study on e-WOM Channels’ Effects on the Saudi Hospitality Market

Abstract

In this paper, we study the relationship between electronic word of mouth (e-WOM), brand perceptions, and consumer purchase intentions in the Saudi hospitality market via an extensive questionnaire design using a five-point Likert scale. A total of 410 respondents from the central, western, and eastern regions of Saudi Arabia were chosen using the convenience sampling technique. The structural equation modeling (SEM) analysis using SPSS AMOS 26 software showed that e-WOM via social media, online retail stores, brand-owned media, and influencers positively affects perceived brand quality, which leads to increased purchase intentions. The model showed an R-squared of 0.579, indicating that e-WOM explains about 57.9% of the variation in perceived brand quality. In particular, e-WOM through social media and influencers has been established as a strong factor in predicting perceptions of brand quality. This study reveals that learning the nuances and strategic management of e-WOM channels is key to improving brand perceptions and consumer purchase behavior in the Saudi hospitality market in the digital age.

1. Introduction

Electronic word of mouth (e-WOM) is irreplaceable, and it has become a critical part of the digital marketing field, bringing customers and industries closer through the internet. Unlike in the past, where WOM required a lot of physical networks and personal contacts, e-WOM now uses digital platforms to improve communications and, consequently, gather more messages regarding a particular brand. This process has become possible because of technology, which can be used to influence more consumers. This digital WOM includes social media word of mouth, online reviews, and influencer-created content. Key platforms where e-WOM thrives include Google, Amazon, Yelp, Instagram, and Facebook. It is also worth noting that online reviews are now the main factor that people consider before purchasing something. Thus, the quantity and quality of responses to feedback are quite critical. It has been observed that a 0.1-star increase can increase conversion rates by 25% [1], which clearly shows the great influence of ratings on consumer behavior.

Compelling statistics underscore the importance of e-WOM, demonstrating that this factor plays a significant role in consumer decision-making processes. For example, 90% of consumers read reviews on the internet before visiting a business. Moreover, 84% of consumers trust brands based on online reviews as much as personal endorsements, and 71% are more likely to purchase a product after being referred to it on social media [2]. These statistics not only illustrate the relevance of e-WOM in the consumer decision-making process but also demonstrate its power as a business strategy for companies that embrace it. As opposed to conventional WOM strategies such as reporting remarkable client experiences or providing referral bonuses, e-WOM functions on a larger scale and has a long-term impact. This lasting impact is vital in business since e-WOM determines how people view brands and purchases after the original post hits the Web.

This research will unravel the complicated influence of e-WOM on the brand quality perceptions and buying habits of consumers in the realm of the hospitality industry in KSA. Adopting the distinctive communication methods employed in e-WOM, this study will reveal the unique differences in the comprehension and behavior of consumers when one form of communication is tested against another. This study was designed to examine the impact of e-WOM channels such as social media, online stores, brand-owned media, and influencers on perceived brand quality and to determine the relationship between perceived product quality and consumer purchase intentions. This study aims to define a model of the effects of e-WOM, providing good empirical proof for marketers and hospitality brand managers to use strategically in the 21st century.

In order to accomplish our research objectives, this article starts with a comprehensive review of the relevant literature, followed by a discussion of the theoretical foundation, the construction of the methodology, the presentation of the findings, and a discussion of how these results relate to earlier research.

2. Literature Review

The literature on electronic word of mouth (e-WOM) through social media highlights its considerable impact on consumer behavior, particularly in shaping purchase intentions. The authors of [3] studied the extent of social media e-WOM. They stressed how peer reviews on websites like Facebook and Twitter affect consumers’ purchasing decisions, pointing out that social media has changed consumers from passive receivers of information to partakers in the dissemination process. However, the degree of influence of e-WOM depends on the individual, and this factor may play a role in consumers’ critical assessment of information. Moreover,. [4] studied the role of CSR communication through social media in shaping e-WOM (word of mouth) among bank customers in India. However, they found that CSR initiatives shared via social networks led to higher consumer engagement and e-WOM intentions, supporting the view that managers exploit the advantages of these platforms to amplify their CSR communication, consequently increasing consumer identification and e-WOM. Furthermore, [5] explored the factors that contribute to attitudes toward e-WOM on social media in China, and the main motivational factors were found to be website quality, social support, emotional experience, and subjective norms. This research highlights the moderating role of personal interactivity and the positive effect of perceived behavioral control on e-WOM, which are very useful in aiding e-WOM strategies in digital marketing via social media.

The examination of electronic word of mouth (e-WOM) within retailer-based stores has drawn more attention. The authors of [6] examined the extent to which the types of discounts, e-WOM, and the range of prices influence purchase intentions among university students in e-commerce environments. The results showed that all of these factors significantly and positively impact consumer attention to e-commerce environments. The study stresses that consumer interest in companies should be considered in depth to come up with pricing and discount strategies, using e-WOM as a weapon. Similarly, [7] studied electronic service quality and e-WOM (electronic word of mouth) as factors determining repurchase intentions in the Indonesian e-commerce industry. These two factors were shown to be positively correlated with service quality and e-WOM creation, influencing consumers’ decisions to repurchase. These findings suggest that an improved focus on producing high-quality electronic services among e-commerce entities results in positive e-WOM and high sales retention. The authors of [8] continued the discussion by investigating the factors influencing customers in Vietnam, broadening the overall debate. This study highlighted the satisfaction of customers as the focal factor determining consumers’ urge to provide e-WOM, identifying the crucial position of e-shopping satisfaction in enabling e-WOM transmission.

Brand-owned media and electronic word of mouth (e-WOM), as extensively studied in the context of Instagram, have remained a central research theme. The authors of [9] explained the interactions between owned media, influencer marketing, and unofficial brand ambassadors, considering the narrative elements of official Ibero-American nation brands and user-generated content (UGC). Their research showed that reels on UGC (user-generated content) accounts achieve dramatically higher engagement than other posts on corporate accounts, which generally produce very limited interaction (mostly comments). Accordingly, it would appear that while UGC and official accounts may garner similar numbers of likes, the former surpass the latter in terms of the deeper connections made through formats such as reels [10,11]. In contrast, [12] sheds more light on e-WOM management within the hotel sector by exploring the strategic use of e-WOM for improving hotel performance. Various case studies also show that the key factors for hotels’ success include using e-WOM analytics for brand reputation and strategic decisions, emphasizing authentic response systems, and improving communication capabilities as competitive advantages. Both studies characterize the shifting picture of e-WOM and the determining nature of brand-owned and user-generated content on digital platforms, playing into consumer engagement and perception formation.

A recent study by [13] suggested that SMIs (social media influencers) employ storytelling mechanisms for the creation of e-WOM (electronic word of mouth) on Chinese social media sites. This research identified several different narrative strategies. The authors of [14,15] demonstrated the important contrast between the credibility of the e-WOM provided by opinion leaders and the credibility of online consumer reviews (OCRs). The source critically asserts that consumers see the latter as more useful and credible, particularly with the increasing online shopping experience. The authors of [16] looked at the impact of celebrity marketing on purchase intentions and brand attitudes, especially among young female audiences. This paper placed special emphasis on the critical role of Instagram posts by digital influencers in shaping consumer behavior and brand perceptions. The authors of [14] also researched the role of opinion leaders’ e-WOM on customer decision-making online, finding that its impact is more significant on purchase decisions for experience-type goods when the e-WOM is positive. However, its effect was limited compared to the control groups. These research findings affirm the complex functions of SOC in the generation of e-WOM and the intricate connections between storytelling techniques, platform environments, and consumer perceptions. Therefore, additional research is necessary to deepen the knowledge of successful e-WOM strategies in the digital era.

How consumers perceive brand quality is a very important factor that governs their behavior and attitudes toward brands, leading to brand loyalty and customer satisfaction. The authors of [17,18] described perceived quality as consumers’ evaluation of one brand’s excellence compared with that of others. The authors of [19], on the other hand, stressed the role of quality in business strategy and described its position in competition, customer choice, and market transformation. The authors of [17] proposed that consumers may develop brand love relationships when the perceived quality is high. The authors of [19] conducted research on the relationship between brand experience and brand loyalty, and the results showed that a positive brand experience can help increase perceived quality and brand trust. Consequently, this can lead to more repeat purchases, especially in the sports consumer sector. The authors of [17,20] investigated the effects of functional brand qualities such as quality and innovativeness on brand value, moderated by brand experience and emotion, respectively. The research team concluded that high-quality and innovative investments drive the brand experience and personality. These factors are valued in the market and determine consumer interactions with brands and products.

Research on end-user purchase intentions in e-commerce spans different segments and product categories, and it is important to marketers and retailers in the online space [21,22,23,24]. The critical factors identified include performance expectancy, effort expectancy, individual innovativeness, health consciousness, and ecological trustworthiness. Other factors identified in studies include functional values, hedonic motivation, price perception, information access, trust, and social influence. Strategies to improve online purchase intentions include reducing consumer anxieties and influencing personal innovativeness. Additionally, some sources consider promoting organic foods based on vehicle drivers, providing full information, building trust, satisfying hedonic motivation, and providing a shopping experience as key strategies. Some studies use the unified theory of acceptance and use of technology (UTAUT) to emphasize the relevance of efficiency, ease of use, and social trendiness as part of online retail marketing strategies [23,24].

3. Theoretical Background and Conceptual Model

3.1. Social Influence Theory

The social influence theory [25,26] explains that people’s attitudes, beliefs, and behaviors are significantly affected by the social interactions and contexts in which they find themselves. The theory identifies three main processes of social influence: compliance, acceptance, and inculcation. Compliance is achieved through alignment with a rewards system or punishment; identification is the effect of people imitating what they admire; and internalization is when people are influenced by values to which they relate. Social influence theory outlines the current study on the effects of online word of mouth (e-WOM) and brand perception on end-users’ buying intentions; it reveals that e-WOM, not only by social media influencers but also by any regular user, significantly impacts consumer attitudes and behavior. Consumers provide e-WOM to support social norms, identify with their preferred influencers, and own a message that reflects their beliefs. This specific application of social influence theory shows how much power e-WOM has over purchasing intentions.

3.2. Information Adoption Model

The information adoption model (IAM) is an extension of the technology acceptance model (TAM) that focuses on how individuals perceive and use the information that they receive in online spaces [27,28,29]. The IAM combines cognitive elaboration and source credibility to foresee the acceptance of information by users, highlighting the features of perceived usefulness and ease of use, source credibility, and information quality [30,31]. In the context of this study, the IAM encompasses the theoretical framework enabling us to evaluate how consumers and their decisions process e-WOM information. Through the IAM, researchers can explore how e-WOM affects the perceived usefulness and ease of use of information; these are both consequences of source credibility, influencing consumers’ adoption of information, as well as their purchase intentions. Thus, this strategy is helpful in understanding the roles of information quality and source credibility in shaping consumer decision-making in the e-WOM market.

Based on the theories explained above, Figure 1 below shows this study’s conceptual model.

4. Hypothesis Development

According to the latest research, e-WOM through social media plays a remarkable role in perceived brand quality; e-WOM (in the form of user-generated content) on social media allows consumers to discuss their experiences and opinions on brands, influencing the views of potential customers [32]. The information adoption model confirms this phenomenon, showing that the credibility and quality of information spread on social media can influence consumers’ judgments about brand quality [33]. In addition, [34] states that e-WOM on social media influences how consumers perceive and feel toward a brand—positive e-WOM can increase perceptions of brand quality, while negative e-WOM can impair them. Thus, e-WOM through social media is crucial in determining perceived brand quality. Therefore, this is an area of focus for brands that want to improve or retain their image in the online marketplace. Our hypothesis, in this context, is as follows:

H1: 

e-WOM via social media has an impact on perceived brand quality.

Research in e-marketing continues to focus on the influence of electronic word of mouth (e-WOM) on perceived brand quality. Through electronic WOM, also known as e-WOM, as consumers share feedback and experiences directly on retailers’ websites, this form of e-WOM can highly influence the reputed quality of the brand. It has been shown that ratings and reviews provided by customers, which are featured on retail websites, greatly affect how potential buyers perceive the quality and credibility of a brand [15,35]. This is because online marketplace sites provide the brand with a direct point of connection with its consumers, where honest and authentic user feedback can directly impact a brand’s image [36]. Therefore, the consolidation of customer feedback in online stores, including both positive and negative reviews, portrays all of the quality features of the brand from the customer’s standpoint, which is quite essential for the overall brand image. Therefore, we contend with the following:

H2: 

e-WOM via retailers’ online stores impacts perceived brand quality.

The phenomenon of e-WOM arising from brand-owned media is increasingly being considered by recent studies in the marketing literature. Brand-based media channels (e.g., official websites and blogs) provide companies with a controlled environment to communicate with consumers directly and curate and share content related to the brand’s values, missions, and the quality of the product [37]. These platforms allow brands to develop and distribute positive e-WOM in the form of testimonials, case studies, and user-generated content, which can significantly enhance the customers’ understanding of brand quality. The authors of [38] argue that the strategic use of brand-owned media to convey true and positive customer experiences can successfully increase consumers’ perceptions of brand quality by maintaining the brand’s message consistency and reinforcing its value proposition. In addition, the interactive element of brand-owned media enables instantaneous feedback and interactions that affect consumer perceptions, preferences, and bona fide status credibility [31]. Thus, we tentatively hypothesize the following:

H3: 

e-WOM via brand-owned media has an impact on perceived brand quality.

Electronic word of mouth (e-WOM) about a brand via influencers has become an essential area of study within digital marketing and consumer behavior research. Influencers, endowed with expertise, credibility, and relatability, serve as pivotal actors in forming and developing audience perceptions toward brands and their product offers [16]. Suppose that influencers share their experiences or endorsements of a brand; in that case, followers will perceive the brand as reputable, owing to the trust and rapport that the influencers have built with their audience [14]. This is also exemplified in parasocial interaction, where followers feel personal affiliation with influencers, making the latter’s suggestions seem more critical [13]. Furthermore, influencers’ authenticity and personal narratives effectively increase perceptions of a brand’s quality, delivering a more humane perspective on the brand’s value proposition [9]. For this reason, we contend that the following:

H4: 

e-WOM via influencers has an impact on perceived brand quality.

The perception of brand quality and its influence on end-user purchasing intentions are well established in studies that highlight the magnitude of the impact of brand quality perceptions on consumers’ purchase decisions. The consumer’s perceived brand quality—a judgment of a brand’s overall superiority and excellence compared to competitors—directly impacts the purchase intent by creating a more attractive brand, thereby reducing the perceived risk [39]. A high-quality product tells the brand’s consumers that it meets or even exceeds their expectations. Therefore, it increases the likelihood of consumers purchasing it [19]. Moreover, research conducted in [40] has extended this notion, presenting strong brand quality perceptions as one of the elements of brand equity that positively impact purchase intentions. This relationship is significant in highly competitive markets that provide a variety of choices for consumers and a high-quality product as the main factor of differentiation [41]. Consequently, we hypothesize the following:

H5: 

Perceived brand quality has an impact on end-user purchasing intentions.

5. Research Methodology

5.1. Item Measurement and Questionnaire Design

The questionnaire contains several multiple-choice questions based on a 5-point Likert agreement–disagreement scale. While the questions are qualitative, their responses are quantitative, as they determine the level of agreement or disagreement that a respondent expresses regarding specific questions. Appendix A lists the questions used by the researchers for soliciting field responses.

5.2. Structural Model

Our structural model comprises six key variables, each with several constructs, as shown in Figure 2. The number of constructs is commensurate with the questions tied to each variable. For example, e-WOM via social media has five constructs, while purchase intention has four. All of the main variables are unobserved, while their respective constructs are observed/measured as derived from the respondents’ opinions. Variables e1 through e25 are residual values capturing the unobserved effects of the variables/relationships that they decorate. The general flow of the model follows the assumption that e-WOM, via different avenues, influences perceived brand quality, which, in turn, influences purchase intention. Figure 2 exemplifies the structural model.

5.3. Population and Sampling

Saudi Arabia’s population is highly concentrated in the western, eastern, and central regions. As shown in

Table 1. Saudi Arabia population analysis.

Province	ISO	Capital	Area (km2)	Population	Density (pers/km2)
’Asir	SA-14	Abha	76,693	2,024,285	26.4
Bahah	SA-11	Al Bahah	9921	339,174	34.2
Eastern Province (As-Sarqiyah)	SA-04	Dammam	672,522	5,125,254	7.6
Ha’il	SA-06	Ha’il	118,232	746,406	6.3
Jawf	SA-12	Sakakah	100,212	595,822	5.9
Jizan	SA-09	Jizan	11,671	1,404,997	120.4
Madinah	SA-03	Medina	151,990	2,389,452	15.7
Makkah	SA-02	Mecca	153,148	7,769,994	50.7
Najran	SA-10	Najran	149,511	592,300	4
Northern Borders (Al Hudud)	SA-08	Ar’ar	111,797	373,577	3.3
Qassim	SA-05	Buraidah	58,046	1,336,179	23
Riyad	SA-01	Riyadh	404,240	8,591,748	21.3
Tabuk	SA-07	Tabuk	146,072	886,036	6.1
Total			2,164,055	32,175,224	14.9

Source: [41].

, the total population is 32.2 million, according to the 2022 census estimate. The most populous regions are Riyadh (central), Makkah (western), and As-Sarqiyah (eastern), which formed the basis of our selection for this investigation.

We sampled 410 hotel clients from 34 hotels in the three regions, with proper consideration of the representative populations of the cities. We employed a simple, convenient sampling technique to identify the respondents who would participate in the study. This is a non-probabilistic method, in which the inclusion criteria are the respondents’ availability and willingness to participate in the study.

5.4. Data Collection

In data collection, we solely relied on the closed-ended questionnaire described in the “Item Measurement and Questionnaire Design” subsection. This was deemed to be the most suitable data collection method because of the nature and size of the data required for analysis. We collected these data within two weeks across the three regions of interest. The process involved approaching hotel management personnel and politely asking them to share the questionnaires with their willing clients. Once they agreed, we required them to express their consent by filling in the “Informed Consent Form” before answering the questions.

6. Data Analysis

6.1. Analysis of Respondent Profiles

In our analysis, we first examined the respondents’ demographic characteristics under three properties: gender, age group, and region of residence.

6.1.1. Gender

We established that most of the hotel client respondents were male, accounting for 58.29% of the 410 respondents. On the other hand, female respondents were significantly fewer, accounting for only 154 (37.56%).

Table 2. Distribution of respondents by gender.

Gender
Male	239	58.29%
Female	154	37.56%
Not Revealed	17	4.15%
	410	100.00%

also reveals that some of the respondents did not reveal their gender, accounting for 4.15% of the sample size. These findings show that the participants were overwhelmingly willing to share their gender and that most of them were male.

6.1.2. Age Group

Secondly, we examined the constitution of the sample size based on the age group criterion. We found (as reported in

Table 3. Distribution of respondents by age group.

Age Group
>50 Yrs	181	44.15%
41–50 Yrs	140	34.15%
31–40 Yrs	56	13.66%
18–30 Yrs	33	8.05%
	410	100.00%

) that the largest group were respondents over 50 years old, of whom there were 181 (44.15%). The least dominant age group was respondents aged between 18 and 30 years; this group comprised 33 members and accounted for 8.05% of the sampled participants. These findings are plausible, as the age of people visiting hotel premises as clients is often middle-aged to older people.

6.1.3. Region of Residence

As mentioned earlier, we carried out our investigation in three distinct regions: central, western, and eastern. Our findings in

Table 4. Distribution of respondents by region.

Region
Central	203	49.51%
Western	137	33.41%
Eastern	70	17.07%
	410	100.00%

suggest that the central region had the greatest number of willing participants, contributing 203 (49.51%) of the respondents. Participants from the western region were next, with 137 (33.41). The least popular was the eastern region, whose 70 participants accounted for 17.07% of the sampled respondents.

6.2. Assessment of the Measurement Model

6.2.1. Assessment of the Structural Model

Using SPSS AMOS 26 software, our analysis produced the model depicted in Figure 3 below, which shows that all of the critical independent variables had a measurable effect on their respective dependent variables. This indicates that the model has some credibility, as can be seen in the deeper analysis in subsequent subsections.

6.2.2. Indicator/Composite Reliability

We conducted a test to establish the composite reliability of the instrument and its respective key variables. This reliability metric measures the latent variable’s overall reliability given its indicator variables’ loadings. Essentially, it estimates the indicator variables’ consistency to ensure their integrity and compactness. The author of [42] found that a composite reliability score of 0.7 or above can be considered acceptable. Our analysis in

Table 5. Constructs’ indicators.

Construct	Items	Factor Loading	L2	Error Variance	Composite Reliability	Indicators	Cronbach’s Alpha	AVE
e-WOM via Social Media	SOC1	0.970	0.9409	0.0591	0.861	5	0.711	0.56
	SOC2	0.803	0.644809	0.355191
	SOC3	0.614	0.376996	0.623004
	SOC4	0.558	0.311364	0.688636
	SOC5	0.737	0.543169	0.456831
e-WOM via Brand-Owned Media	MED1	0.968	0.937024	0.062976	0.823	3	0.792	0.62
	MED2	0.719	0.516961	0.483039
	MED3	0.627	0.393129	0.606871
e-WOM via Retailers’ Online Stores	STO1	0.651	0.423801	0.576199	0.812	3	0.707	0.59
	STO2	0.816	0.665856	0.334144
	STO3	0.830	0.6889	0.3111
e-WOM via Influencers	INF1	0.852	0.725904	−0.028196	0.863	4	0.740	0.61
	INF2	0.750	0.5625	0.248311
	INF3	0.706	0.498436	0.314416
	INF4	0.815	0.664225	0.429975
Perceived Brand Quality	PBQ1	1.014	1.028196	−0.028196	0.926	4	0.921	0.76
	PBQ2	0.867	0.751689	0.248311
	PBQ3	0.828	0.685584	0.314416
	PBQ4	0.755	0.570025	0.429975
End-User Purchase Intention	PUR1	0.761	0.579121	0.420879	0.918	4	0.914	0.74
	PUR2	0.847	0.717409	0.282591
	PUR3	0.835	0.697225	0.302775
	PUR4	0.981	0.962361	0.037639

shows results suggesting that all of the construct variables are reliable. The highest score was for perceived brand quality (0.926), while the lowest was for retailer online stores (0.812), as shown in Table 5 below.

6.2.3. Internal Consistency

We also examined the internal consistency of the key variables used in the structural equation model (SEM). We employed Cronbach’s alpha, a reliability metric specifically used to assess internal consistency. According to [42], if the alpha falls below 0.7, the instrument’s internal consistency is questionable. Our findings in Table 5 put all of the tests for the critical variables above 0.7, while the overall alpha was 0.798. This implies that our questionnaire is steadfast in its ability to withstand retests in different environments.

6.2.4. Convergent Validity

The third item of analysis in assessing the measurement model was convergent validity, which questions the relatedness of constructs measuring the same variable. In this case, the goal is for such constructs to have high levels of relatedness. The metric often used to measure convergent validity is average variance extracted (AVE). According to [43], if the AVE is above 0.5, the convergent validity of the variable is affirmed. In our analysis, we observed that all of the variables scored AVE values greater than 0.5, as shown in Table 5.

6.2.5. Discriminant Validity

Our analysis went a step further to examine the discriminant validity of the responses captured in the questionnaires. This metric examines the independence of the responses for each key variable relative to the responses from other variables [42]. Independence is established if the overall correlation coefficient between constructs of the same variables shows higher values than when compared to the correlation that they have with other variables’ constructs. Our findings suggest that all constructs demonstrated stark independence, as illustrated in

Table 6. Results of discriminant validity analysis.

	SOC	MED	STO	INF	PBQ	PUR
SOC	0.8983
MED	0.4814	0.7075
STO	0.6973	0.5631	0.8404
INF	0.4217	0.6705	0.6359	0.7955
PBQ	0.7222	0.3934	0.4495	0.5118	0.7301
PUR	0.6715	0.5346	0.4465	0.407	0.4878	0.7852

below. Notably, the variable with the highest level of independence of constructs was e-WOM via social media (0.8983), while the least independent was e-WOM via brand-owned media (0.7075).

6.3. Regression Analysis

We considered it prudent to run a regression analysis to capture the individual effects of the key independent variables on the dependent variables.

6.3.1. e-WOM’s Effects on Perceived Brand Quality

In

Table 7. Multiple regression analysis (e-WOM and brand quality).

Statistic	Value
R	0.761
R-Squared	0.579
Adjusted R-Squared	0.575
Std. Error of the Estimate	0.60723
Regression Sum of Squares	205.667
Residual Sum of Squares	149.337
Total Sum of Squares	355.004
F Value	139.442
Significance	0.000
Coefficient for SOC	B = 0.512, Beta = 0.305, Sig. = 0.000
Coefficient for INF	B = 0.575, Beta = 0.451, Sig. = 0.000
Coefficient for STO	B = 0.291, Beta = 0.232, Sig. = 0.000
Coefficient for MED	B = 0.215, Beta = 0.178, Sig. = 0.001

, we report the results of a multiple regression analysis pitting e-WOM via social media, e-WOM via retailer online stores, and e-WOM via brand-owned media against perceived brand quality. Our findings in Table 7 reveal that the model was significant, as evidenced by the F value of 139.442 (p = 0.000, df = 409). The R-squared coefficient was 0.579, signifying that e-WOM via the three identified channels accounted for 57.9% of the variation in perceived brand quality. While all e-WOM channels impacted perceived brand quality significantly at a 95% confidence interval (CI), the most notable was e-WOM via social media, which scored a beta of 0.565 (p = 0.000, n = 410). The least influential was e-WOM via brand-owned media, scoring a beta of 0.178 (p = 0.001, n = 410).

6.3.2. Effect of Perceived Brand Quality on Purchase Intention

In this subsection, we report the outcome of regressing purchase intention on perceived brand quality. The findings in

Table 8. Simple regression analysis (brand quality and purchase intention).

Statistic	Value
R	0.763
R-Squared	0.582
Adjusted R-Squared	0.581
Std. Error of the Estimate	0.47299
F Value	567.568
Significance (Sig.)	0.000
Coefficients for PBQ (Constant)	B = 1.785, Beta = 0.763, Sig. = 0.000

suggest that the R-squared coefficient is 0.582. This value implies that perceived brand quality accounts for 58.2% of the variation in consumer purchase intention in Saudi Arabia. The significance of PBQ is affirmed by the beta coefficient scored by the variable of 0.763 (p = 0.000, n = 410).

6.4. Model Fit Indices

The model fit indices suggest a formidable structural equation model. We considered the opinion of [43] as the criterion for assessing the significance of our model; this source claims that a structural equation with the default model’s CMIN/DF of less than 5.0 is sufficiently formidable. Our findings in

Table 9. Model fit index metrics.

Model	NPAR	CMIN	DF	P	CMIN/DF
Default Model	42	508.37	148	0.000	3.435
Saturated Model	190	0.000	0.000
Independence Model	19	2604.037	171	0.000	15.228

indicate that the default model scored a CMIN of 508.37 (p = 0.000, df = 148). Computing the CMIN/DF gives the metric 3.435, which is significantly lower than the suggested threshold of 5.0. For this reason, we are satisfied with its performance in predicting perceived brand quality and consumers’ purchase intentions in the Kingdom of Saudi Arabia.

6.5. Decisions on Hypotheses

The findings from the analysis herein have a bearing on the hypotheses developed in the initial stages of this research. As such,

Table 10. Summary of decisions on hypotheses.

Hypothesis		Results (p-Value)	Decision
H1.	SOC → PBQ	0.000	Accept
H2.	MED → PBQ	0.000	Accept
H3.	STO → PBQ	0.001	Accept
H4.	INF → PBQ	0.000	Accept
H5.	PBQ → PUR	0.000	Accept

shows a breakdown of the decisions made on each hypothesis.

7. Discussion, Implications, Limitations, and Future Research

7.1. Discussion and Implications

In this study, we conducted structural equation modeling of the relationship between e-WOM, perceived brand quality, and consumers’ purchase intentions in Saudi Arabia. Our findings strongly suggest that our model is significant in estimating all relationships.

We established that electronic word of mouth spread via social media platforms effectively influences consumer perceptions of brands in Saudi Arabia. This factor scored the highest in influencing brand perceptions compared to brand-owned media and retailer online stores. These findings are consistent with those of previous studies carried out on similar topics. There are profound similarities to the findings presented in [32]. In both cases, the researchers agreed that opinions shared on social media have a significant potential to influence consumer perceptions of products and services. A similar view was shared by [33], thereby strengthening the argument. Given the extent of social media coverage in the kingdom, there is an opportunity for tourism and hospitality websites to mold consumer perceptions of their brands by enhancing their activity and presence on these platforms. It is high time that businesses in Saudi Arabia use social media as a professional tool to attain a competitive advantage by influencing users’ emotions in favor of their business interests.

Brand-owned media also featured heavily in our analysis. We established that this channel significantly influences consumer perceptions about brands and products. While it was the least influential among its peers, its statistical significance was credible enough to suggest formidability. In [38], similar findings were established, in that the researchers lauded brand-owned media as a factor worth considering because of its relationship with brand quality perception. The authors of [37] also argued along these lines, claiming that the strategic use of brand-owned media to convey authentic and positive customer experiences can successfully increase the consumers’ perceptions of brand quality by maintaining the brand’s message consistency and reinforcing its value proposition. We can infer from this discussion that the spread of electronic word of mouth in Saudi Arabia influences consumer perceptions of brand quality, and that the Saudi hospitality market should fit it within their strategic framework.

We also examined the effect of e-WOM via retailers’ online stores on perceived brand quality. In the context of our study, we defined these stores as online portals owned and maintained by tourism and hospitality websites from which customers can interact with the businesses to buy, communicate, or view published information. Our findings suggested that this channel significantly influences consumer opinion and sentiments on brands and products. As demonstrated in the existing literature, there seems to be a host of research publications agreeing with this position. The authors of [35] contend that ratings and reviews provided by customers, which are featured on retail websites, significantly affect how potential buyers perceive a brand’s quality and credibility. This view is also shared by the authors of [36], who claimed that online marketplace sites provide the brand with a direct point of connection with its consumers, where honest and authentic user feedback can directly impact the brand’s image. This discussion implies that businesses in Saudi Arabia can substantially benefit from hosting product reviews on their online stores.

Regarding the effect of electronic word of mouth from influencers on perceived brand quality, our analysis provided critical insights into how this relationship manifests. We dwelled on the trustworthiness and authenticity of information published by influencers, regardless of the means they used. Our findings suggested that this channel was the second-most formidable in its ability to mold consumer sentiments on brands. These results are consistent with the findings in [14], where the researchers reported that when influencers share their experiences or endorsements of a brand, their followers perceive the brand as reputable, owing to the trust and rapport that the influencers have built with their audience. Similarly, [9] contributes to this debate by pointing out that influencers’ authenticity and personal narratives effectively increase the quality perception of a brand by delivering a more humane perspective on the brand’s value proposition. This implies that influencers greatly influence how consumers perceive brands, especially on digital platforms. Hence, this research highlights the business benefits of engaging with social influencers, as brand reputation improves with their input.

The analysis of the effect of perceived brand quality on purchase intention formed a significant part of this research. Our findings suggested that a more positive perception of a brand often increases the chances of a consumer purchasing a product. This position seems to be consistent with the findings of several pieces of research in the existing literature. The authors of [39] agree with this notion and add that a product that is perceived to be of high quality tells the brand’s consumers that it meets or exceeds their expectations. Since all rational consumers seek to maximize their utility, they are expected to opt for products that they perceive as being highly likely to fulfill this desire. Another study asserting similar sentiments is [41], which claims that brand perception is critical in highly competitive markets that provide a variety of choices for consumers, and that product quality may be the main factor of differentiation. The implication is that firms determined to enhance consumer perceptions of brand quality tend to inspire more purchases from their clientele. As such, the Saudi hospitality market could significantly improve its sales by engaging in this approach.

7.2. Limitations

We must acknowledge that a few areas of this research could have worked against the integrity of our findings. One such area is our choice of sampling technique: convenience sampling. This strategy involves selecting survey participants based on their proximity and availability. The model is non-probabilistic and can result in skewed results, yet it was impossible to create a list of potential participants in advance to help in random selection. Nevertheless, we took the necessary actions to make our selection as random as possible. Additionally, we must acknowledge that this research was mainly quantitative, which has its own disadvantages. Given the nature of the topic under investigation, it would have been more insightful to include some qualitative information. However, we enriched the questions by making them qualitative but with multiple choices. Thus, while the questions were qualitative (i.e., asking about people’s sentiments about specific issues), we also solicited numerical responses. We are confident that our mitigation efforts extensively limited the effects that these issues could have had on the research outcomes.

7.3. Conclusions

This study demonstrates a robust link between e-WOM through different channels and perceived brand quality (PBQ), which determines purchase intention. Our findings reveal that e-WOM through social media is a potent predictor of PBQ, which emphasizes the importance of social media in forming the opinions of consumers. Additionally, e-WOM via influencers has a strong positive effect, supporting the idea that influencer endorsements may significantly affect perceptions of brand quality. Even though e-WOM via brand-owned media was found to be only slightly related to the overall brand quality, it still affects the perception of a brand. The same applies to e-WOM via retailer-owned stores. The composite reliability scores of each construct proved to be consistently reliable within the SEM. The findings and discussion support the two theories of social influence and information adoption models. Hence, these two frameworks are still relevant in the marketing sphere and should be considered as a guide for strategic marketing decisions. Our findings provide evidence for marketers to develop strategies to implement better approaches to improve their brand perception and create purchase intentions in the digital universe.

7.4. Future Research

Given the ongoing research on electronic word of mouth (e-WOM), subsequent studies should consider the differences in the sectoral impacts of e-WOM across diverse domains beyond the hospitality sector so as to generalize the present findings. As it turns out, studying the effects of e-WOM on new products or market segments, such as technology or fashion, could provide more information. Longitudinal studies could determine whether perceptions of brand quality are influenced by e-WOM over time, observing that the digital landscape is quickly changing. Moreover, quantitative research could also be carried out to expand the comprehension of consumers’ mental processes, making them influencers of e-WOM actions. It would be appropriate to enhance the cross-culture application of such studies to determine regional influences on the impacts of e-WOM. Furthermore, widening the scope to include the consequences of negative e-WOM and the methods of its handling could provide extensive strategies for managing brand reputation resilience.